Predictive alerts for individual risk of injury with ameliorative actions

ABSTRACT

Methods and systems for predicting injury risk include generating state sequences that precede a hazard event based on information regarding a user&#39;s state. A cognitive suite of workplace hygiene and injury predictors (WHIP) is generated based on the state sequences using a processor, wherein the cognitive WHIP predicts a degree of risk correlated with each particular user state sequence. A risk heat map of a workplace is generated based on the cognitive WHIP that encodes regions of greater and lesser risk. An ameliorative action is triggered when a user moves into an area of high risk based on the heat map.

BACKGROUND

Technical Field

The present invention relates to predicting dangerous conditions and,more particularly, to providing cognitive workplace hygiene and injurypredictors to guide safety decisions.

Description of the Related Art

There are about 350,000 annual workplace fatalities and 270 millionannual workplace injuries worldwide. In the United States alone, thisresults in about $750 billion in lost wages and productivity, medicalexpenses, administrative costs, motor vehicle damage, employers'uninsured costs, and fire loss. These numbers include about 4,400 workerdeaths due to job injuries, close to 50,000 deaths due to work-relatedinjuries, and approximately four million workers who suffer non-fatalwork-related injuries or illnesses. An estimated 14 million peopleworked in the United States manufacturing sector in 2010, with 329deaths due to job injuries, $1.4 million in costs associated with eachdeath, and 127, 140 non-fatal injuries involving days away from work.

In 2008, contact with objects and equipment was the leading cause ofworkplace death and the leading cause of non-fatal injuries involvingdays away from work in the United States manufacturing sector.Overexertion is the second leading cause of non-fatal injuries involvingdays away from work. Although these injuries are widespread, thereexists no reliable way to adaptively learn about risk factors andprovide warnings in real-time.

SUMMARY

A method for predicting injury risk includes generating state sequencesthat precede a hazard event based on information regarding a user'sstate. A cognitive suite of workplace hygiene and injury predictors(WHIP) is generated based on the state sequences using a processor,wherein the cognitive WHIP predicts a degree of risk correlated witheach particular user state sequence. A risk heat map of a workplace isgenerated based on the cognitive WHIP that encodes regions of greaterand lesser risk. An ameliorative action is triggered when a user movesinto an area of high risk based on the heat map.

A method for predicting injury risk includes generating state sequencesthat precede a hazard event based on information regarding a user'sstate including user biometric information from a device worn by theuser and a user's location from one or more workplace monitoringdevices. A cognitive suite of workplace hygiene and injury predictors(WHIP) is generated based on the state sequences using a processor andsupervised learning. The cognitive WHIP predicts a degree of riskcorrelated with each particular user state sequence. A risk heat map ofa workplace is generated based on the cognitive WHIP that encodesregions of greater and lesser risk. An ameliorative action is triggeredwhen a user moves into an area of high risk based on the heat map.

A system for predicting injury risk includes a cognitive suite ofworkplace hygiene and injury predictors (WHIP) module comprising aprocessor configured to generate state sequences that precede a hazardevent based on information regarding a user's state and to generate acognitive WHIP based on the state sequences, wherein the cognitive WHIPpredicts a degree of risk correlated with each particular user statesequence. A heat map module is configured to generate a risk heat map ofa workplace based on the cognitive WHIP that encodes regions of greaterand lesser risk. An alert module is configured to trigger anameliorative action when a user moves into an area of high risk based onthe cognitive WHIP.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The disclosure will provide details in the following description ofpreferred embodiments with reference to the following figures wherein:

FIG. 1 is a block diagram of a workplace risk management system inaccordance with the present principles;

FIG. 2 is a heat map showing a risk in a workplace in accordance withthe present principles;

FIG. 3 is a block/flow diagram of a method for managing risk in aworkplace in accordance with the present principles;

FIG. 4 is a block diagram of a risk prediction system in accordance withthe present principles;

FIG. 5 is a diagram of a cloud computing environment according to thepresent principles; and

FIG. 6 is a diagram of abstraction model layers according to the presentprinciples.

DETAILED DESCRIPTION

Embodiments of the present invention use wearable sensors to gatherinformation about workers and how they behave while performing tasks inthe workplace. This information is coupled with analytics to provide thebasis for optimizing tasks for health and safety. The presentembodiments correlate behavioral and wearable physiological measures ofemployee states in, e.g., a workplace environment, with injury-relatedevents to create a set of predictors of these events that processinformation streams continually and in real-time.

It is to be understood in advance that, although this disclosureincludes a detailed description on cloud computing, implementation ofthe teachings recited herein are not limited to a cloud computingenvironment. Rather, embodiments of the present invention are capable ofbeing implemented in conjunction with any other type of computingenvironment now known or later developed.

Referring now to FIG. 1, a monitoring and feedback system 100 is shown.A user device 102 is worn by a user or otherwise directly monitors theuser's state with one or more sensors 104. In one example, the userdevice is a wristband equipped with a heart rate sensor and anaccelerometer. Other sensors 104 that may be employed include location,time, skin conductivity, moisture, temperature, brainwave, and any othersensor that acquires biometric information about the user.

A user device 102 communicates information collected from the sensors104 back to a remote server 110 via a network access point 108. It isparticularly contemplated that the user device 102 communicates with thenetwork access point 108 via continuous wireless communication, but itis also contemplated that the user device 102 may communicateperiodically or at scheduled intervals or via a wired medium. It shouldnaturally be understood that multiple user devices 102 may be employedin a single workplace, for example worn by each worker. Other workplacemonitoring sensors 106 may additionally gather data about the user inthe workplace and send it to the remote server 110 via the networkaccess point 108. The workplace monitoring sensors 106 may include, forexample, video cameras or audio sensors, worker positioning sensors,ambient condition sensors, etc.

The network access point 108 may communicate with the remote server 110via any appropriate medium, including through the internet. It should beunderstood that the remote server 110 may be a single, centralizedserver or may alternatively be a decentralized set of devices. In onespecifically contemplated embodiment, the remote server 110 isimplemented as a cloud computing solution that includes a large set ofdistributed computing devices that are provisioned as needed to meetdemand.

The remote server 110 performs analytics on the information acquired bythe user device 102 and workplace monitoring 106. User states(including, e.g., their physical, cognitive, and emotional state) aredetermined based on the collected biometric information and theworkplace monitoring information and categorized using unsupervisedlearning. Upon the occurrence of, for example, an industrial hygiene orinjury event, the states are further categorized using supervisedlearning to identify state sequences that precede or do not precedethese events. The sequences of user states that predict events (forexample, a decreased heart-rate that might indicate drowsiness) arecompiled as a cognitive suite of workplace hygiene and injury predictors(abbreviated herein as cognitive WHIPs).

Cognitive and emotional states that are used to form the cognitive WHIPsare defined as functions of measures of a user's total behaviorcollected over some period of time from at least one personalinformation collector (including musculoskeletal gestures, speechgestures, eye movements, internal physiological changes, measured byimaging devices, microphones, physiological and kinematic sensors in ahigh dimensional measurement space) within a lower dimensional featurespace. In one exemplary embodiment, certain feature extractiontechniques are used for identifying certain cognitive and emotionaltraits. Specifically, the reduction of a set of behavioral measures oversome period of time to a set of feature nodes and vectors, correspondingto the behavioral measures' representations in the lower dimensionalfeature space, is used to identify the emergence of a certain cognitiveand emotional traits over that period of time.

The cognitive WHIP outputs are used to create, for example, a heat mapof a factory floor or other workplace environment showing higher andlower degrees of predicted risk of injury or other incidents. The mapmay be shared with workplace supervisors to use in improving safetyconditions. In addition, the remote server 110 may communicateinformation back to the user device 102 to provide one or more alerts tothe user in the event that the user enters a high-risk state. Forexample, an alert 105 in the user device 102 may include an audio (e.g.,an alarm or spoken warning) or visual (e.g, a flashing light or textualmessage) indicator. The remote server 110 may also provide its cognitiveWHIP outputs to other automation technology in the factory for thepurpose of automatically adapting to changing circumstances.

Referring now to FIG. 2, an exemplary risk heat map 202 is shown with alegend 204. The heat map is formed of a number of different regionsincluding, for example, a lowest intensity region 206 and a highestintensity region 208. The legend 204 shows a gradation of intensities ofrisk from lowest at the bottom to highest at the top. The legend 204 canbe discretely divided, as shown, or may instead have a continuousgradation from low risk to high risk.

Given such a map, supervisors can investigate the high-risk regions 208to determine if something can be done to decrease or mitigate the risk.Additionally, information and warnings can be provided to users whoventure into the high-risk regions 208, ranging from a general alert tospecific risk mitigation instructions.

Referring now to FIG. 3, a method for predicting risk of injury isshown. Block 302 gathers data from sensors 104 on user devices 102 andblock 304 gathers data from workplace monitoring sensors 106 via anyappropriate medium. Block 306 gathers information about hazard events,including industrial hygiene events and worker injuries. Block 308 thenlearns state sequences, based on the data gathered about the users, thatprecede a hazard event. From these sequences, block 310 generates acognitive WHIP that adaptively predicts whether a given state sequencecorrelates to a high risk.

Block 312 uses the cognitive WHIP to generate a risk heat map 202 of theworkplace. Block 314 then provides alerts to users who move intohigh-risk areas, including for example providing a visual or auditoryalert, providing a textual description of the risk and any ameliorativeor mitigating action that can be taken, notifying management of ahigh-risk situation, and triggering any automatic safety measures thatare appropriate. Block 314 may also include a processor andreinforcement learning for adjusting future alerts based on outcomes ofpast alerts.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Reference in the specification to “one embodiment” or “an embodiment” ofthe present principles, as well as other variations thereof, means thata particular feature, structure, characteristic, and so forth describedin connection with the embodiment is included in at least one embodimentof the present principles. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment”, as well any other variations,appearing in various places throughout the specification are notnecessarily all referring to the same embodiment.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “A/B”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended, as readily apparent by one of ordinaryskill in this and related arts, for as many items listed.

Referring now to FIG. 4, a system 400 for predicting risk of injury isshown. The risk prediction system 400 may be implemented as the remoteserver 110 described above, or may be a distributed cloud system asdescribed below. The system 400 includes a hardware processor 402 andmemory 404. The system 400 also includes a set of functional modulesthat may be implemented, for example, as software stored in memory 404and executed by the hardware processor 402. Alternatively, the modulesmay be implemented as one or more discrete hardware components, forexample as application specific integrated chips or field programmablegate arrays.

The risk prediction system 400 collects and stores information about theusers from user devices 102 and workplace monitoring devices 106 togenerate user state sequences. These user state sequences 406 mayrepresent any appropriate set of data relating to the user's actions andphysical state within the workplace and are stored in memory 404. Acognitive WHIP module 408 builds cognitive WHIPs based on the user statesequences that lead to hazard events. The cognitive WHIPs generated bycognitive WHIP module 408 predict risk based on a current user state,taking into account, for example, user position, user emotional state,user physical state, user cognitive state, and any other factor whichthe user state sequences 406 have access to.

A heat map module 410 uses the cognitive WHIPs to map regions of theworkplace to risk levels in a heat map 202. The heat map 202 encodes anabsolute risk level of the regions, but may also include contextualfactors based on an individual user. For example, a given region may beparticularly hazardous to users who are drowsy. The heat map 202 maytherefore encode information pertaining to user states. An alert module412 issues alerts that trigger ameliorative action. The ameliorativeaction may include sending information to user devices 102 andmanagement responsive to a user's operation within a high-risk area. Theameliorative action may also include triggering a change in setting of adevice or activating a device in the workplace to reduce a degree ofrisk in the high-risk area.

Referring now to FIG. 5, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 6 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and risk prediction 96.

Having described preferred embodiments of predictive alerts forindividual risk of injury with ameliorative actions (which are intendedto be illustrative and not limiting), it is noted that modifications andvariations can be made by persons skilled in the art in light of theabove teachings. It is therefore to be understood that changes may bemade in the particular embodiments disclosed which are within the scopeof the invention as outlined by the appended claims. Having thusdescribed aspects of the invention, with the details and particularityrequired by the patent laws, what is claimed and desired protected byLetters Patent is set forth in the appended claims.

1. A method for predicting injury risk, comprising: generating statesequences that precede a hazard event based on information regarding auser's state; generating a cognitive suite of workplace hygiene andinjury predictors (WHIP) based on the state sequences using a processor,wherein the cognitive WHIP predicts a degree of risk correlated witheach particular user state sequence; generating a risk heat map of aworkplace based on the cognitive WHIP that encodes regions of greaterand lesser risk; and triggering an ameliorative action when a user movesinto an area of high risk based on the heat map.
 2. The method of claim1, wherein generating the cognitive WHIP comprises determining statesequences that are related to the hazard event by supervised learning.3. The method of claim 1, wherein triggering the ameliorative actioncomprises activating one or more automated system to decrease ormitigate risk to the user.
 4. The method of claim 1, wherein triggeringthe ameliorative action comprises providing information to the userregarding the area of high risk.
 5. The method of claim 1, whereintriggering the ameliorative action comprises changing a setting in oneor more device in the workplace to decrease a risk in the area of highrisk.
 6. The method of claim 1, further comprising gathering user stateinformation from a device worn by the user.
 7. The method of claim 6,wherein the user state information comprises biometric informationrelating to at least one measurable aspect of the user's physical orcognitive state.
 8. The method of claim 1, further comprising gatheringuser state information from one or more workplace monitoring devices. 9.The method of claim 8, wherein the user state information comprises userlocation within the workplace.
 10. A computer readable storage mediumcomprising a computer readable program for predicting injury risk,wherein the computer readable program when executed on a computer causesthe computer to perform the steps of claim
 1. 11. A method forpredicting injury risk, comprising: generating state sequences thatprecede a hazard event based on information regarding a user's stateincluding user biometric information from a device worn by the user anda user's location from one or more workplace monitoring devices;generating a cognitive suite of workplace hygiene and injury predictors(WHIP) based on the state sequences using a processor and supervisedlearning, wherein the cognitive WHIP predicts a degree of riskcorrelated with each particular user state sequence; generating a riskheat map of a workplace based on the cognitive WHIP that encodes regionsof greater and lesser risk; and triggering an ameliorative action when auser moves into an area of high risk based on the heat map.
 12. A systemfor predicting injury risk, comprising: a cognitive suite of workplacehygiene and injury predictors (WHIP) module comprising a processorconfigured to generate state sequences that precede a hazard event basedon information regarding a user's state and to generate a cognitive WHIPbased on the state sequences, wherein the cognitive WHIP predicts adegree of risk correlated with each particular user state sequence; aheat map module configured to generate a risk heat map of a workplacebased on the cognitive WHIP that encodes regions of greater and lesserrisk; and an alert module configured to trigger an ameliorative actionwhen a user moves into an area of high risk based on the cognitive WHIP.13. The system of claim 12, wherein the cognitive WHIP is furtherconfigured to determining state sequences that are related to the hazardevent by supervised learning.
 14. The system of claim 12, wherein thealert module is further configured to activate one or more automatedsystem to decrease or mitigate risk to the user.
 15. The system of claim12, wherein the alert module is further configured to provideinformation to the user regarding the area of high risk.
 16. The systemof claim 12, wherein the alert module is further configured to change asetting in one or more device in the workplace to decrease a risk in thearea of high risk.
 17. The system of claim 12, wherein the cognitiveWHIP module is further configured to gather user state information froma device worn by the user.
 18. The system of claim 17, wherein the userstate information comprises biometric information relating to at leastone measurable aspect of the user's physical or cognitive state.
 19. Thesystem of claim 12, wherein the cognitive WHIP module is furtherconfigured to gather user state information from one or more workplacemonitoring devices.
 20. The system of claim 19, wherein the user stateinformation comprises user location within the workplace.